PhD Chapter 3
Results 2/3
This series of files compile all analyses done during Chapter 3:
- Section 1 presents the calculation of the indices of exposure.
- Section 2 presents variable exploration and regressions results.
- Section 3 presents species distribution models.
All analyses have been done with R 4.0.2.
Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it
Sources of activity considered for the analyses:
- aquaculture: mussel farm (AquaInf)
- city: general diffusive influence, wharves (CityInf, CityWha)
- industry: general diffusive influence, wharves (Indu, InduWha)
- sediment dredging: collection zones, dumping zones (DredColl, DredDump)
- commercial shipping: mooring sites, traffic routes (ShipMoor, ShipTraf)
- sewers: rainwater drains, wastewater drains (SewRain, SewWast)
Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):
| Gear | Code | Years | Events | Species |
|---|---|---|---|---|
| Dredge | FishDred | 2010-2014 | 21 | Mactromeris polynyma |
| Net | FishNet | 2010 | 5 | Clupea harengus, Gadus morhua |
| Trap | FishTrap | 2010-2015 | 1061 | Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus |
| Bottom-trawl | FishTraw | 2013-2014 | 2 | Pandalus borealis |
1. Spatial variation of exposure indices
Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).
Aquaculture
## Model selected: Sph
## nugget = 0; sill = 0.00704; range = 7.01955; kappa = 0.5
City
## Model selected: Lin
## nugget = 0.00025; sill = 0.00602; range = 8.57222; kappa = 0.5
Sediment dredging
## Model selected: Exp
## nugget = 0.00021; sill = 0.02042; range = 4.52941; kappa = 0.5
Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.0072; range = 10.10924; kappa = 0.5
Sewers
## Model selected: Exp
## nugget = 0; sill = 0.03366; range = 43.15003; kappa = 0.5
Shipping
## Model selected: Lin
## nugget = 0; sill = 0.06455; range = 4.27615; kappa = 0.5
Fisheries: Dredge
## Model selected: Lin
## nugget = 0; sill = 0.01019; range = 2.81568; kappa = 0.5
Fisheries: Net
## Model selected: Exp
## nugget = 2e-05; sill = 0.00456; range = 0.70613; kappa = 0.5
Fisheries: Trap
## Model selected: Lin
## nugget = 0.00034; sill = 0.00128; range = 1.12045; kappa = 0.5
Fisheries: Bottom-trawling
## Model selected: Lin
## nugget = 0; sill = 0.03509; range = 3.90932; kappa = 0.5
2. Relationships with abiotic parameters
2.1. Covariation
Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.
⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.
Aquaculture
City
Sediment dredging
Industry
Sewers
Shipping
Fisheries: Dredge
Fisheries: Net
Fisheries: Trap
Fisheries: Bottom-trawling
Cumulative exposure
2.2. Correlation
Correlations have been calculated with Spearman’s rank coefficient.
| om | gravel | sand | silt | clay | arsenic | cadmium | chromium | copper | iron | manganese | mercury | lead | zinc | S | N | B | H | J | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aquaculture | -0.439 | 0.167 | 0.477 | -0.444 | -0.048 | -0.688 | -0.784 | -0.737 | -0.668 | -0.62 | -0.772 | -0.766 | -0.722 | -0.733 | 0.311 | 0 | -0.029 | 0.345 | 0.188 |
| city | -0.155 | -0.067 | 0.427 | -0.273 | -0.096 | -0.246 | -0.163 | -0.171 | 0.086 | -0.004 | -0.154 | -0.243 | -0.167 | -0.015 | -0.108 | -0.036 | -0.153 | -0.055 | 0.035 |
| dredging | 0.275 | -0.084 | -0.091 | 0.103 | 0.055 | 0.264 | 0.19 | 0.407 | 0.574 | 0.649 | 0.55 | 0.219 | 0.324 | 0.482 | -0.215 | -0.133 | 0.049 | -0.13 | -0.023 |
| industry | 0.159 | -0.071 | -0.016 | 0.045 | 0.069 | 0.176 | 0.115 | 0.348 | 0.514 | 0.588 | 0.504 | 0.157 | 0.253 | 0.405 | -0.246 | -0.115 | 0.053 | -0.198 | -0.076 |
| sewers | 0.254 | -0.037 | -0.313 | 0.268 | 0.249 | 0.609 | 0.581 | 0.654 | 0.694 | 0.591 | 0.707 | 0.579 | 0.689 | 0.689 | -0.353 | -0.063 | 0.021 | -0.369 | -0.174 |
| shipping | 0.456 | -0.249 | -0.291 | 0.314 | -0.015 | 0.537 | 0.504 | 0.618 | 0.693 | 0.677 | 0.708 | 0.549 | 0.576 | 0.687 | -0.19 | -0.06 | 0.022 | -0.172 | -0.095 |
| fisheries_dredge | -0.238 | 0.068 | 0.246 | -0.241 | -0.045 | -0.465 | -0.458 | -0.558 | -0.602 | -0.648 | -0.649 | -0.42 | -0.474 | -0.578 | 0.334 | 0.028 | -0.084 | 0.423 | 0.228 |
| fisheries_net | 0.004 | -0.055 | -0.158 | 0.119 | 0.191 | 0.078 | -0.001 | 0.055 | 0.033 | 0.055 | 0.106 | 0.01 | 0.036 | 0.026 | -0.112 | -0.137 | 0.069 | -0.035 | 0.127 |
| fisheries_trap | -0.503 | 0.158 | 0.422 | -0.38 | -0.095 | -0.444 | -0.346 | -0.323 | -0.318 | -0.291 | -0.301 | -0.353 | -0.376 | -0.358 | 0.077 | 0.182 | -0.032 | -0.062 | -0.169 |
| fisheries_trawl | -0.215 | 0.172 | 0.088 | -0.182 | -0.105 | -0.237 | -0.306 | -0.349 | -0.451 | -0.368 | -0.466 | -0.313 | -0.308 | -0.397 | 0.216 | -0.009 | -0.038 | 0.162 | 0.032 |
| cumulative_exposure | 0.241 | -0.095 | -0.107 | 0.145 | 0.035 | 0.259 | 0.131 | 0.283 | 0.407 | 0.44 | 0.375 | 0.185 | 0.298 | 0.373 | -0.026 | -0.039 | 0.006 | -0.043 | -0.096 |
| om | gravel | sand | silt | clay | arsenic | cadmium | chromium | copper | iron | manganese | mercury | lead | zinc | S | N | B | H | J | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aquaculture | 2.038e-06 | 0.08404 | 1.82e-07 | 1.453e-06 | 0.6241 | 1.878e-16 | 1.123e-23 | 1.028e-19 | 2.749e-15 | 8.733e-13 | 1.446e-22 | 4.639e-22 | 1.119e-18 | 1.952e-19 | 0.001047 | 0.999 | 0.7653 | 0.0002525 | 0.05113 |
| city | 0.1087 | 0.4921 | 3.982e-06 | 0.004225 | 0.3206 | 0.01043 | 0.09275 | 0.07674 | 0.3781 | 0.964 | 0.1118 | 0.01126 | 0.08362 | 0.8744 | 0.2674 | 0.7138 | 0.1134 | 0.5708 | 0.7171 |
| dredging | 0.004038 | 0.3876 | 0.3492 | 0.2869 | 0.574 | 0.005687 | 0.04861 | 1.217e-05 | 8.309e-11 | 2.962e-14 | 6.813e-10 | 0.02309 | 0.000633 | 1.283e-07 | 0.02517 | 0.171 | 0.6121 | 0.1789 | 0.8096 |
| industry | 0.1007 | 0.465 | 0.8689 | 0.6459 | 0.4811 | 0.06919 | 0.2347 | 0.0002275 | 1.3e-08 | 2.144e-11 | 2.783e-08 | 0.1043 | 0.008203 | 1.389e-05 | 0.01022 | 0.236 | 0.5892 | 0.03971 | 0.4341 |
| sewers | 0.007974 | 0.702 | 0.000962 | 0.004998 | 0.009281 | 2.623e-12 | 4.439e-11 | 1.762e-14 | 8.768e-17 | 1.703e-11 | 1.192e-17 | 5.084e-11 | 1.659e-16 | 1.805e-16 | 0.000176 | 0.5189 | 0.8284 | 8.325e-05 | 0.07195 |
| shipping | 7.165e-07 | 0.009324 | 0.002213 | 0.0009345 | 0.8743 | 2.146e-09 | 2.655e-08 | 1.041e-12 | 1.003e-16 | 9.205e-16 | 1.105e-17 | 7.68e-10 | 7.258e-11 | 2.359e-16 | 0.04853 | 0.5351 | 0.8202 | 0.07554 | 0.3296 |
| fisheries_dredge | 0.01314 | 0.4825 | 0.01028 | 0.01193 | 0.6404 | 3.925e-07 | 6.196e-07 | 3.496e-10 | 5.797e-12 | 3.49e-14 | 2.894e-14 | 6.176e-06 | 2.193e-07 | 5.928e-11 | 0.0004168 | 0.7711 | 0.3857 | 5.068e-06 | 0.01743 |
| fisheries_net | 0.9713 | 0.5721 | 0.1025 | 0.2201 | 0.04787 | 0.4215 | 0.9885 | 0.573 | 0.7361 | 0.5728 | 0.2767 | 0.9196 | 0.7104 | 0.7874 | 0.2496 | 0.1576 | 0.4781 | 0.7212 | 0.1906 |
| fisheries_trap | 2.878e-08 | 0.1014 | 5.265e-06 | 4.889e-05 | 0.3305 | 1.481e-06 | 0.0002478 | 0.0006488 | 0.0008039 | 0.002278 | 0.001548 | 0.0001765 | 6.138e-05 | 0.0001419 | 0.4277 | 0.05927 | 0.7393 | 0.524 | 0.0798 |
| fisheries_trawl | 0.02573 | 0.07593 | 0.3644 | 0.05997 | 0.2811 | 0.0134 | 0.001257 | 0.0002149 | 9.741e-07 | 8.969e-05 | 3.712e-07 | 0.0009717 | 0.001194 | 2.129e-05 | 0.0248 | 0.9286 | 0.6962 | 0.09349 | 0.7425 |
| cumulative_exposure | 0.01215 | 0.3291 | 0.2723 | 0.1337 | 0.7181 | 0.006716 | 0.1762 | 0.003005 | 1.219e-05 | 1.843e-06 | 6.524e-05 | 0.05463 | 0.001741 | 6.932e-05 | 0.7919 | 0.6858 | 0.9505 | 0.655 | 0.3225 |
3. Relationships with benthic communities
The most abundant taxa in our study area are:
- Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
- Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)
The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density or biomass.
Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 2.015, and the five categories are from ‘bad’ to ‘high’, with 20 %, 40 %, 60 % or 80 % of the maximum exposure.
By exposure gradient
By exposure categories
| Phylum | low | bad | moderate | high | good |
|---|---|---|---|---|---|
| Annelida | 15.2 | 26.8 | 40.6 | 27.4 | 29 |
| Arthropoda | 13.4 | 39.2 | 55.3 | 44.3 | 1 |
| Cnidaria | 0 | 0 | 0 | 0 | 1 |
| Echinodermata | 0.2 | 3.04 | 3.5 | 0.96 | 105 |
| Mollusca | 12 | 9.92 | 19.2 | 12.4 | 19 |
| Nematoda | 0 | 0.458 | 6.14 | 17.2 | 3 |
| Nemertea | 0 | 0.167 | 0 | 0.24 | 0 |
| Sipuncula | 0.4 | 0.417 | 0.357 | 0.14 | 0 |
| Phylum | low | bad | moderate | high | good |
|---|---|---|---|---|---|
| Annelida | 3.2 | 0.913 | 2.35 | 0.621 | 0.0737 |
| Arthropoda | 0.0221 | 0.0666 | 0.11 | 0.173 | 1e-04 |
| Cnidaria | 0 | 0 | 0 | 0 | 3.36 |
| Echinodermata | 0.00436 | 3.79 | 2.09 | 8.96 | 0.455 |
| Mollusca | 1.8 | 0.517 | 2.39 | 1.29 | 1.14 |
| Nematoda | 0 | 3.75e-05 | 0.000511 | 0.00067 | 3e-04 |
| Nemertea | 0 | 0.0712 | 0 | 4.4e-05 | 0 |
| Sipuncula | 0.0168 | 0.0175 | 0.00519 | 0.00891 | 0 |
4. Relationships with community characteristics
The following graphs present the distribution of community characteristics along index of cumulative exposure.
4.1. Data manipulation
For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.
All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| aquaculture | 1 | 0.061 | -0.355 | -0.299 | -0.666 | -0.696 | 0.724 | 0.017 | 0.319 | 0.571 |
| city | 0.061 | 1 | 0.334 | 0.325 | 0.131 | 0.22 | -0.304 | -0.043 | -0.008 | -0.366 |
| dredging | -0.355 | 0.334 | 1 | 0.961 | 0.668 | 0.686 | -0.685 | 0.038 | -0.089 | -0.475 |
| industry | -0.299 | 0.325 | 0.961 | 1 | 0.691 | 0.598 | -0.662 | 0.097 | 0.044 | -0.475 |
| sewers | -0.666 | 0.131 | 0.668 | 0.691 | 1 | 0.65 | -0.747 | 0.165 | -0.162 | -0.563 |
| shipping | -0.696 | 0.22 | 0.686 | 0.598 | 0.65 | 1 | -0.688 | 0.037 | -0.379 | -0.641 |
| fisheries_dredge | 0.724 | -0.304 | -0.685 | -0.662 | -0.747 | -0.688 | 1 | -0.08 | 0.142 | 0.514 |
| fisheries_net | 0.017 | -0.043 | 0.038 | 0.097 | 0.165 | 0.037 | -0.08 | 1 | 0.135 | -0.071 |
| fisheries_trap | 0.319 | -0.008 | -0.089 | 0.044 | -0.162 | -0.379 | 0.142 | 0.135 | 1 | 0.23 |
| fisheries_trawl | 0.571 | -0.366 | -0.475 | -0.475 | -0.563 | -0.641 | 0.514 | -0.071 | 0.23 | 1 |
4.2. Univariate regressions
We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models).
We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the table below:
| Human activity | S | N | B | H | J |
|---|---|---|---|---|---|
| Aquaculture | |||||
| City | |||||
| Dredging | - | + | + | ||
| Industry | |||||
| Sewers | - | - | - | - | |
| Shipping | + | ||||
| Fisheries: Dredge | + | + | |||
| Fisheries: Net | |||||
| Fisheries: Trap | |||||
| Fisheries: Bottom-trawling | + | - | |||
| Adjusted \(R^{2}\) | 0.19 | 0.01 | 0.01 | 0.15 | 0.06 |
Details of the regressions, with diagnostics and cross-validation, are summarized below.
Richness
## FULL MODEL
## Adjusted R2 is: 0.16
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -4.095e-16 | 0.08829 | -4.638e-15 | 1 | |
| aquaculture | 0.09202 | 0.116 | 0.7934 | 0.4295 | |
| city | -0.02657 | 0.1093 | -0.243 | 0.8085 | |
| dredging | -0.003504 | 0.1137 | -0.03083 | 0.9755 | |
| industry | -0.1208 | 0.1373 | -0.8798 | 0.3811 | |
| sewers | -0.1956 | 0.1402 | -1.395 | 0.1661 | |
| shipping | 0.1581 | 0.102 | 1.55 | 0.1243 | |
| fisheries_dredge | 0.2228 | 0.1016 | 2.192 | 0.03078 | * |
| fisheries_net | -0.002718 | 0.0891 | -0.0305 | 0.9757 | |
| fisheries_trap | 0.04523 | 0.1011 | 0.4472 | 0.6557 | |
| fisheries_trawl | 0.1314 | 0.09398 | 1.398 | 0.1652 |
## RMSE from cross-validation: 45.57618
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.31 | 1.23 | 1.28 | 1.55 | 1.58 | 1.15 | 1.15 | 1 | 1.14 | 1.06 |
## REDUCED MODEL
## Adjusted R2 is: 0.19
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -4.262e-16 | 0.08663 | -4.92e-15 | 1 | |
| sewers | -0.3081 | 0.0983 | -3.134 | 0.002246 | * * |
| shipping | 0.1338 | 0.09332 | 1.434 | 0.1547 | |
| fisheries_dredge | 0.2483 | 0.09596 | 2.588 | 0.01105 | * |
| fisheries_trawl | 0.1321 | 0.09064 | 1.457 | 0.1481 |
## RMSE from cross-validation: 0.9119322
| sewers | shipping | fisheries_dredge | fisheries_trawl | |
|---|---|---|---|---|
| VIF | 1.13 | 1.07 | 1.1 | 1.04 |
Density
## FULL MODEL
## Adjusted R2 is: -0.03
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 2.035e-16 | 0.09753 | 2.087e-15 | 1 | |
| aquaculture | -0.04097 | 0.1281 | -0.3198 | 0.7498 | |
| city | 0.114 | 0.1208 | 0.9443 | 0.3474 | |
| dredging | -0.1131 | 0.1256 | -0.9005 | 0.3701 | |
| industry | -0.2065 | 0.1517 | -1.362 | 0.1765 | |
| sewers | 0.2371 | 0.1548 | 1.531 | 0.1289 | |
| shipping | -0.1087 | 0.1126 | -0.9652 | 0.3369 | |
| fisheries_dredge | 0.03383 | 0.1123 | 0.3013 | 0.7638 | |
| fisheries_net | -0.03855 | 0.09842 | -0.3916 | 0.6962 | |
| fisheries_trap | 0.02044 | 0.1117 | 0.1829 | 0.8552 | |
| fisheries_trawl | 0.05671 | 0.1038 | 0.5463 | 0.5861 |
## RMSE from cross-validation: 69.01764
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.31 | 1.23 | 1.28 | 1.55 | 1.58 | 1.15 | 1.15 | 1 | 1.14 | 1.06 |
## REDUCED MODEL
## Adjusted R2 is: 0.01
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 1.936e-16 | 0.09571 | 2.023e-15 | 1 | |
| dredging | -0.1414 | 0.09615 | -1.47 | 0.1444 |
## RMSE from cross-validation: 1.00622
| dredging | |
|---|---|
| VIF | 1 |
Biomass
## FULL MODEL
## Adjusted R2 is: -0.02
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -6.145e-17 | 0.09734 | -6.313e-16 | 1 | |
| aquaculture | -0.154 | 0.1279 | -1.204 | 0.2314 | |
| city | -0.1777 | 0.1205 | -1.474 | 0.1437 | |
| dredging | -0.01418 | 0.1253 | -0.1132 | 0.9101 | |
| industry | 0.1864 | 0.1514 | 1.231 | 0.2212 | |
| sewers | -0.3287 | 0.1546 | -2.127 | 0.03598 | * |
| shipping | -0.1268 | 0.1124 | -1.128 | 0.2623 | |
| fisheries_dredge | -0.09693 | 0.1121 | -0.865 | 0.3892 | |
| fisheries_net | -0.01053 | 0.09823 | -0.1071 | 0.9149 | |
| fisheries_trap | 0.03059 | 0.1115 | 0.2744 | 0.7844 | |
| fisheries_trawl | -0.01579 | 0.1036 | -0.1524 | 0.8792 |
## RMSE from cross-validation: 1.029382
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.31 | 1.23 | 1.28 | 1.55 | 1.58 | 1.15 | 1.15 | 1 | 1.14 | 1.06 |
## REDUCED MODEL
## Adjusted R2 is: 0.01
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -4.956e-17 | 0.09577 | -5.175e-16 | 1 | |
| sewers | -0.1366 | 0.09622 | -1.419 | 0.1587 |
## RMSE from cross-validation: 0.992684
| sewers | |
|---|---|
| VIF | 1 |
Diversity
## FULL MODEL
## Adjusted R2 is: 0.12
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 1.732e-16 | 0.09017 | 1.92e-15 | 1 | |
| aquaculture | 0.1144 | 0.1184 | 0.9657 | 0.3366 | |
| city | -0.01046 | 0.1117 | -0.09372 | 0.9255 | |
| dredging | 0.184 | 0.1161 | 1.586 | 0.1161 | |
| industry | -0.1202 | 0.1402 | -0.8569 | 0.3936 | |
| sewers | -0.2888 | 0.1432 | -2.017 | 0.04643 | * |
| shipping | 0.1179 | 0.1041 | 1.132 | 0.2604 | |
| fisheries_dredge | 0.1574 | 0.1038 | 1.516 | 0.1327 | |
| fisheries_net | 0.04753 | 0.09099 | 0.5223 | 0.6026 | |
| fisheries_trap | -0.02014 | 0.1033 | -0.195 | 0.8458 | |
| fisheries_trawl | -0.03562 | 0.09597 | -0.3711 | 0.7113 |
## RMSE from cross-validation: 14.90696
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.31 | 1.23 | 1.28 | 1.55 | 1.58 | 1.15 | 1.15 | 1 | 1.14 | 1.06 |
## REDUCED MODEL
## Adjusted R2 is: 0.15
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | 1.615e-16 | 0.08872 | 1.82e-15 | 1 | |
| dredging | 0.1395 | 0.09707 | 1.437 | 0.1538 | |
| sewers | -0.3605 | 0.1022 | -3.528 | 0.0006249 | * * * |
| fisheries_dredge | 0.1674 | 0.09678 | 1.729 | 0.0867 |
## RMSE from cross-validation: 0.9333495
| dredging | sewers | fisheries_dredge | |
|---|---|---|---|
| VIF | 1.09 | 1.15 | 1.09 |
Evenness
## FULL MODEL
## Adjusted R2 is: 0.01
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -6.312e-17 | 0.09567 | -6.597e-16 | 1 | |
| aquaculture | 0.04889 | 0.1257 | 0.389 | 0.6981 | |
| city | 0.03175 | 0.1185 | 0.268 | 0.7892 | |
| dredging | 0.2245 | 0.1232 | 1.823 | 0.07142 | |
| industry | -0.1216 | 0.1488 | -0.8173 | 0.4157 | |
| sewers | -0.1962 | 0.1519 | -1.292 | 0.1995 | |
| shipping | -0.00884 | 0.1105 | -0.08001 | 0.9364 | |
| fisheries_dredge | 0.04924 | 0.1101 | 0.4471 | 0.6558 | |
| fisheries_net | 0.04615 | 0.09655 | 0.478 | 0.6337 | |
| fisheries_trap | -0.08131 | 0.1096 | -0.742 | 0.4599 | |
| fisheries_trawl | -0.14 | 0.1018 | -1.375 | 0.1722 |
## RMSE from cross-validation: 86.80069
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.31 | 1.23 | 1.28 | 1.55 | 1.58 | 1.15 | 1.15 | 1 | 1.14 | 1.06 |
## REDUCED MODEL
## Adjusted R2 is: 0.06
| Estimate | Std. Error | t value | Pr(>|t|) | ||
|---|---|---|---|---|---|
| (Intercept) | -6.642e-17 | 0.09322 | -7.125e-16 | 1 | |
| dredging | 0.1702 | 0.1017 | 1.673 | 0.09726 | |
| sewers | -0.2962 | 0.1031 | -2.872 | 0.004941 | * * |
| fisheries_trawl | -0.1478 | 0.09602 | -1.54 | 0.1267 |
## RMSE from cross-validation: 1.031223
| dredging | sewers | fisheries_trawl | |
|---|---|---|---|
| VIF | 1.09 | 1.1 | 1.03 |
Annelid density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.09
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.341 | 0.01892 | 176.6 | 0 | * * * |
| aquaculture | 0.02054 | 0.02303 | 0.8917 | 0.3725 | |
| city | 0.05554 | 0.02162 | 2.569 | 0.01021 | * |
| dredging | -0.1322 | 0.02965 | -4.458 | 8.255e-06 | * * * |
| industry | -0.2923 | 0.0386 | -7.573 | 3.635e-14 | * * * |
| sewers | 0.1486 | 0.03242 | 4.585 | 4.539e-06 | * * * |
| shipping | 0.04719 | 0.01867 | 2.527 | 0.0115 | * |
| fisheries_dredge | -0.07775 | 0.02503 | -3.106 | 0.001895 | * * |
| fisheries_net | -0.06061 | 0.02284 | -2.653 | 0.007977 | * * |
| fisheries_trap | 0.1069 | 0.01679 | 6.366 | 1.944e-10 | * * * |
| fisheries_trawl | -0.2405 | 0.03317 | -7.252 | 4.117e-13 | * * * |
## Unbiased RMSE from cross-validation: 36.44478
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.36 | 1.41 | 1.29 | 1.63 | 1.65 | 1.14 | 1.19 | 1 | 1.34 | 1.05 |
## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.09
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.341 | 0.01891 | 176.6 | 0 | * * * |
| city | 0.05086 | 0.02099 | 2.424 | 0.01537 | * |
| dredging | -0.1329 | 0.0297 | -4.473 | 7.712e-06 | * * * |
| industry | -0.2885 | 0.03834 | -7.525 | 5.269e-14 | * * * |
| sewers | 0.1378 | 0.03006 | 4.585 | 4.538e-06 | * * * |
| shipping | 0.0434 | 0.01818 | 2.387 | 0.01698 | * |
| fisheries_dredge | -0.07202 | 0.02365 | -3.046 | 0.002321 | * * |
| fisheries_net | -0.06083 | 0.02284 | -2.663 | 0.007746 | * * |
| fisheries_trap | 0.1092 | 0.01661 | 6.577 | 4.798e-11 | * * * |
| fisheries_trawl | -0.2415 | 0.03311 | -7.295 | 2.986e-13 | * * * |
## Unbiased RMSE from cross-validation: 36.27681
| city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.37 | 1.29 | 1.62 | 1.53 | 1.11 | 1.15 | 1 | 1.32 | 1.05 |
Arthropod density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.605 | 0.01719 | 209.7 | 0 | * * * |
| aquaculture | -0.1442 | 0.02529 | -5.702 | 1.184e-08 | * * * |
| city | 0.2193 | 0.01832 | 11.97 | 5.197e-33 | * * * |
| dredging | -0.1221 | 0.02332 | -5.236 | 1.641e-07 | * * * |
| industry | -0.7072 | 0.03413 | -20.72 | 2.063e-95 | * * * |
| sewers | 0.7842 | 0.02651 | 29.58 | 2.353e-192 | * * * |
| shipping | -0.09841 | 0.01625 | -6.056 | 1.395e-09 | * * * |
| fisheries_dredge | 0.1249 | 0.01394 | 8.96 | 3.26e-19 | * * * |
| fisheries_net | -0.06479 | 0.02029 | -3.193 | 0.001407 | * * |
| fisheries_trap | -0.0755 | 0.01685 | -4.48 | 7.451e-06 | * * * |
| fisheries_trawl | 0.06447 | 0.0157 | 4.107 | 4.016e-05 | * * * |
## Unbiased RMSE from cross-validation: 97.52826
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.24 | 1.28 | 1.24 | 1.97 | 2.03 | 1.11 | 1.12 | 1 | 1.22 | 1.07 |
## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.19
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.605 | 0.01719 | 209.7 | 0 | * * * |
| aquaculture | -0.1442 | 0.02529 | -5.702 | 1.184e-08 | * * * |
| city | 0.2193 | 0.01832 | 11.97 | 5.197e-33 | * * * |
| dredging | -0.1221 | 0.02332 | -5.236 | 1.641e-07 | * * * |
| industry | -0.7072 | 0.03413 | -20.72 | 2.063e-95 | * * * |
| sewers | 0.7842 | 0.02651 | 29.58 | 2.353e-192 | * * * |
| shipping | -0.09841 | 0.01625 | -6.056 | 1.395e-09 | * * * |
| fisheries_dredge | 0.1249 | 0.01394 | 8.96 | 3.26e-19 | * * * |
| fisheries_net | -0.06479 | 0.02029 | -3.193 | 0.001407 | * * |
| fisheries_trap | -0.0755 | 0.01685 | -4.48 | 7.451e-06 | * * * |
| fisheries_trawl | 0.06447 | 0.0157 | 4.107 | 4.016e-05 | * * * |
## Unbiased RMSE from cross-validation: 95.18765
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.24 | 1.28 | 1.24 | 1.97 | 2.03 | 1.11 | 1.12 | 1 | 1.22 | 1.07 |
Mollusc density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.19
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 2.464 | 0.03033 | 81.25 | 0 | * * * |
| aquaculture | 0.09909 | 0.0289 | 3.428 | 0.000607 | * * * |
| city | 0.2318 | 0.03029 | 7.651 | 1.988e-14 | * * * |
| dredging | -0.07629 | 0.04075 | -1.872 | 0.0612 | |
| industry | 0.2448 | 0.03505 | 6.984 | 2.867e-12 | * * * |
| sewers | -0.2919 | 0.04435 | -6.582 | 4.647e-11 | * * * |
| shipping | -0.2821 | 0.04408 | -6.399 | 1.567e-10 | * * * |
| fisheries_dredge | 0.09612 | 0.01978 | 4.859 | 1.179e-06 | * * * |
| fisheries_net | 0.06228 | 0.02529 | 2.462 | 0.01381 | * |
| fisheries_trap | 0.009764 | 0.02451 | 0.3983 | 0.6904 | |
| fisheries_trawl | 0.01857 | 0.02577 | 0.7208 | 0.4711 |
## Unbiased RMSE from cross-validation: 17.65345
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.32 | 1.56 | 1.52 | 1.52 | 1.46 | 1.21 | 1.13 | 1.01 | 1.36 | 1.06 |
## REDUCED MODEL
## McFadden's pseudo-R2 is: 0.19
| Estimate | Std. Error | z value | Pr(>|z|) | ||
|---|---|---|---|---|---|
| (Intercept) | 3.605 | 0.01719 | 209.7 | 0 | * * * |
| aquaculture | -0.1442 | 0.02529 | -5.702 | 1.184e-08 | * * * |
| city | 0.2193 | 0.01832 | 11.97 | 5.197e-33 | * * * |
| dredging | -0.1221 | 0.02332 | -5.236 | 1.641e-07 | * * * |
| industry | -0.7072 | 0.03413 | -20.72 | 2.063e-95 | * * * |
| sewers | 0.7842 | 0.02651 | 29.58 | 2.353e-192 | * * * |
| shipping | -0.09841 | 0.01625 | -6.056 | 1.395e-09 | * * * |
| fisheries_dredge | 0.1249 | 0.01394 | 8.96 | 3.26e-19 | * * * |
| fisheries_net | -0.06479 | 0.02029 | -3.193 | 0.001407 | * * |
| fisheries_trap | -0.0755 | 0.01685 | -4.48 | 7.451e-06 | * * * |
| fisheries_trawl | 0.06447 | 0.0157 | 4.107 | 4.016e-05 | * * * |
## Unbiased RMSE from cross-validation: 93.28045
| aquaculture | city | dredging | industry | sewers | shipping | fisheries_dredge | fisheries_net | fisheries_trap | fisheries_trawl | |
|---|---|---|---|---|---|---|---|---|---|---|
| VIF | 1.24 | 1.28 | 1.24 | 1.97 | 2.03 | 1.11 | 1.12 | 1 | 1.22 | 1.07 |